Evidence map›Paper›PMID 42736456›Full record

ReviewActa pharmacologica Sinica2026

The RNA structural code: orchestrating gene expression and enabling precision therapies.

Nan-Nan Fang, Qu-Bo Zhu

Abstract readReview
PubMed Publisher
In one paragraph

Review in Acta pharmacologica Sinica, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Nan-Nan FangXiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, China.
Qu-Bo ZhuXiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, China. qubozhu@csu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Following transcription, RNA undergoes hierarchical folding mediated by base pairing and long-range interactions, forming diverse dynamic structures such as local helices, loops, bulges, pseudoknots, G-quadruplexes, riboswitches, and higher-order conformations. These structures function as molecular switches that are recognized by RNA-binding proteins and regulatory factors, thereby precisely controlling the spatiotemporal dynamics of posttranscriptional gene expression. Emerging evidence indicates that aberrant RNA structural dynamics are closely associated with diverse human diseases. RNA-targeted therapeutic strategies, characterized by high specificity, programmability, and broad potential, have emerged as a promising next-generation therapeutic modality beyond conventional small-molecule and antibody-based therapies. Although current RNA-targeted approaches have focused primarily on gene silencing, strategies for enhancing endogenous gene expression are increasingly demonstrating substantial translational potential. In this review, we systematically summarize posttranscriptional gene regulation mechanisms mediated by RNA structural diversity, with a particular emphasis on the roles of RNA structures in alternative splicing, RNA localization and transport, translation, and RNA degradation. We further discuss recent advances, current challenges, and emerging clinical prospects of RNA-targeted therapeutic strategies in human diseases. Overall, this review provides a comprehensive overview of RNA structure‒function relationships and highlights their implications for precision medicine and the development of next-generation RNA-based therapeutics.

Indexed as

gene expression regulationnon-coding RNApost-transcriptional controlprecision medicineRNA structureRNA-targeted therapy

Identifiers

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.